Debugging Tools in R with Michael Hannon

Today at Davis R Users’ Group, Michael Hannon gave a great talk on how to use R’s native debugging functions. Here are his notes and code.

Introduction

This is a discussion of debugging techniques in R. It is based on a paper by Roger Peng, now at Johns Hopkins University (http://www.biostat.jhsph.edu/~rpeng/docs/R-debug-tools.pdf)

Focus on five functions:

• traceback
• debug
• browser
• trace
• recover

Severity level

R mainly uses two ways of reporting a problem:

• Warning: Does not stop execution
• Error: More serious – does stop execution

Example of warning:

> zzz <- log(-1)

## Warning: NaNs produced

> zzz

## [1] NaN

Example of error:

#### Define a function to be used/abused later on.
message <- function(x) {
  if(x > 0)
    print("Hello")
  else
    print("Goodbye")
  }

Here’s the function in action:

x <- 42
message(x)

## [1] "Hello"

x <- log(-1)  #### Produces warning but does not stop

## Warning: NaNs produced

message(x)

## Error: missing value where TRUE/FALSE needed

Debugging tools

Print “call stack” with traceback

How did we get here? That is, what sequence of function calls led us to the point where an error occurred? Answer: the traceback function can tell us. Here’s an example:

>   message(log(-1))
Error in if (x > 0) print("Hello") else print("Goodbye") : 
  missing value where TRUE/FALSE needed
In addition: Warning message:
In log(-1) : NaNs produced
> 
>   traceback()
1: message(log(-1))

It’s hard to get “lost” when you’ve got only one function to begin with, but real life is more complicated. THe following isn’t exactly “real-life” either, but it better illustrates the point.

f <- function(x) {
    r <- x - g(x)
    r
}
g <- function(y) {
    r <- y * h(y)
    r
}
h <- function(z) {
    r <- log(z)
    if (r < 10) 
        r^2 else r^3
}

You can probably guess that the log function is going to be the trouble maker, but let’s see what happens.

> f(-1)
Error in if (r < 10) r^2 else r^3 : missing value where TRUE/FALSE needed
In addition: Warning message:
In log(z) : NaNs produced
> traceback()
3: h(y) at #2
2: g(x) at #2
1: f(-1)
> 

This shows that we started out with a call to f, which generated a call to g, which generated a call to h. The error evidently occurred at statement #2 of h. Note, by the way, the following trick to view the line numbers in a function:

> as.list(body(h))
[[1]]
`{`

[[2]]
r <- log(z)

[[3]]
if (r < 10) r^2 else r^3

I.e., statement #2 of h is, as expected, the one that involves the log function.

The debug function

The traceback function helps us to locate the error, but then what do we do? Ideally, we could just stare at our code, then find and fix the error. But it often helps to have some detailed information as to what was going on in our code at the time of the error.

The debug function allows us to do just that. We can step through our code, a line at a time, and examine variables along the way.

The syntax is:

debug(function-name)

and to turn off debugging we use:

undebug(function-name)

(I.e., we might find and fix a problem on the third pass through a loop of 10000 iterations, after which we no longer need or want to debug for the other 9997 times through the loop.)

Here’s an example. The mathematical background is that we have N observations of a random variable that we assume to be normally distributed but with an unknown mean and variance. To estimate the parameters we need to compute the sum of the squared differences from the mean:

\[ SS = \sum_{n=1}^{N}(x_n - \mu)^2 \]

(Details at: http://en.wikipedia.org/wiki/Normal_distribution#Estimation_of_parameters)

Here we’ll focus on just one part of the calculation, computing the sum of the squared differences. Here’s an R function that will do that for us:

SS <- function(mu, x) {
    d <- x - mu
    d2 <- d^2
    ss <- sum(d2)
    ss
}

You can probably see that the function could be written much more compactly, but we’ll string it out for purposes of illustration.

Let’s generate some points from a Normal(0,1) distribution:

set.seed(314159)  # Set the RNG seed so results are reproducible
xPoints <- rnorm(100)

Let’s run SS to get a feel for its behavior:

SS(1, xPoints)

## [1] 202.5

muTest <- as.numeric(-5:5)
SSTest <- sapply(muTest, SS, xPoints)
plot(muTest, SSTest)

Now suppose we want to examine in detail the behavior of the SS function. First, we flag the function for debugging:

debug(SS)

> SS(1, xPoints)
debugging in: SS(1, xPoints)
debug at #1: {
    d <- x - mu
    d2 <- d^2
    ss <- sum(d2)
    ss
}
Browse[2]> n
debug at #2: d <- x - mu
Browse[2]> n
debug at #3: d2 <- d^2
Browse[2]> n
debug at #4: ss <- sum(d2)
Browse[2]> n
debug at #5: ss
Browse[2]> n
exiting from: SS(1, xPoints)
[1] 202.482

This isn’t a particularly interesting use of the debugger, as all we did was to step through the function a line at a time, using the ‘n’ command, but it does illustrate some of the basic ideas:

• First, when we enter the SS function, the function itself is printed, and execution stops.

• The R prompt, ‘>’, changes to ‘Browse[]>’

There are four basic commands you can use in the debugger:

• n: execute the current line of code and step to the next

• c: continue execution without stopping again

• Q: quit debugging completely and to back to the top-level R prompt

• where: similar to calling traceback

You can execute any R code at the “Browse” prompt, i.e., not just code related to the function. The most important example of this is probably the ls() command, which shows us what objects are defined in the current environment (i.e., within a given function, for instance):

    Browse[2]> ls()
    [1] "mu" "x" 

    Browse[2]> mu
    [1] 1

    Browse[2]> head(x)
    [1] -0.79928868 -0.73009689  1.43687692  0.30502316 -0.39728401
    [6]  0.08889039

But the facility is completely general. Here’s a trivial example of defining something unrelated to the current function:

    Browse[2]> zzz <- sqrt(64)
    Browse[2]> zzz
    [1] 8

Here’s another, somewhat more interesting session with SS:

    > SS(1, xPoints)
    debugging in: SS(1, xPoints)
    debug at #1: {
        d <- x - mu
        d2 <- d^2
        ss <- sum(d2)
        ss
    }
    Browse[2]> n
    debug at #2: d <- x - mu
    Browse[2]> n
    debug at #3: d2 <- d^2
    Browse[2]> d[1]  ## Print the value of the first element of d
    [1] -1.799289
    Browse[2]> n
    debug at #4: ss <- sum(d2)
    Browse[2]> hist(d2)
    Browse[2]> n
    debug at #5: ss
    Browse[2]> print(ss)
    [1] 202.482
    Browse[2]> ls()
    [1] "d"  "d2" "mu" "ss" "x" 
    Browse[2]> where
    where 1: SS(1, xPoints)
    
    Browse[2]> y <- x^2  ## Create new object
    Browse[2]> ls()
    [1] "d"  "d2" "mu" "ss" "x"  "y" 
    Browse[2]> head(y)
    [1] 0.638862388 0.533041463 2.064615286 0.093039131 0.157834589
    [6] 0.007901501
    Browse[2]> c  ## Execute the rest of the function w/o stopping
    exiting from: SS(1, xPoints)
    [1] 202.482

    > undebug(SS)  ## No need for further debugging

Note the explicit use of the print function in the above. At an interactive prompt, R will “autoprint” the value of a variable when it is used, so use of the print function is optional. But what if, for instance, you had n as a parameter in your function? Then to examine n you’d need to say explicitly:

print(n)

One final thing: if you want to debug a function, you have to say so before you call the function. This is more or less obvious.

Perhaps not so obvious is the fact that you can flag functions for debugging “on the fly”. I.e., you can turn on debugging for other functions while you’re in the process of debugging a given function. Example later.

Explicit Calls to Browser

Rather than flagging a function for debugging and then stepping through the function a line at a time, it is sometimes convenient to modify your code in such a way as to trigger debugging at the point where you suspect the trouble lies.

Consider the following modified version of SS, for instance. We might feel confident that the trouble occurs in our function only after d2 has been calculated, so we insert a call to the function browser at the point where we want to start, well, browsing.

SS <- function(mu, x) {
    d <- x - mu
    d2 <- d^2
    browser()
    ss <- sum(d2)
    ss
}

    > SS(2, xPoints)
    Called from: SS(2, xPoints)
    Browse[1]> ls()
    [1] "d"  "d2" "mu" "x" 
    Browse[1]> print(mu)
    [1] 2
    Browse[1]> mean(x)
    [1] -0.05553729
    Browse[1]> n
    debug at #5: ss <- sum(d2)
    Browse[2]> c
    [1] 513.5895

One drawback to this mode of operation is that you have to remember to remove the call to browser before you put the code into production.

One real advantage of this approach is that you can use it to debug “anonymous” functions, as:

    sapply(1:50, function_with_no_name {
        .
        .
        .
        browser()
        .
        .
        .
    })

Inserting Code with trace

As with debug, the function trace allows you to debug code, but trace is much more general and sophisticated. The simplest uses of trace don’t appear much different from the corresponding use of debug, although some differences are evident.

The following example is taken from the book Software for Data Analysis by John M. Chambers. Suppose we have defined three functions, called Fn1, Fn2, Fn3. (The details of the function don’t matter for the purposes of this example.) Then we might use trace as follows:

    trace(Fn1, recover)
    trace(Fn2, exit = browser)
    trace(Fn3, browser, exit = browser)

Here, browser is the same function we saw above. The function recover lets us examine the full sequence of calls that brought us to a given point in our code, and then browse inside any of the functions in the sequence.

After the above calls to trace, all future calls to Fn1 will begin with a call to recover(); calls to Fn2 will invoke browser() just before the function exits; calls to Fn3 will invoke browser() on both entry to the function and exit from the function. (In the case of Fn3 we might get tired of stepping through the function and hit c (continue), but stop before we leave the function, juuust to make sure everything is still OK before we lose all the local variables in the function.)

The trace function works by modifying the code of the function, then saving the modified function, and running the modified version when you invoke the function. The original code is not touched, and you can restore the original behavior by executing:

    untrace(Fn1)
    etc.

Here are some examples with our SS function:

trace(SS, recover)
SS(1, xPoints)
untrace(SS)

trace(SS, browser)
SS(2, xPoints)
untrace(SS)

trace(SS, exit = browser)
SS(3, xPoints)
untrace(SS)

The paper by Roger Peng, mentioned at the beginning of this note, has an example in which he minimizes the negative log-likelihood of a statistical model, using an R function that he calls nLL. The details of the discussion are somewhat complicated and will be omitted here.

The salient point is that the procedure in question involves many iterations, and is plagued by the same problem that we discussed above: the log of a negative number produces a so-called NaN (“not a number”) in some of the iterations. We would hope not to get NaN’s in our calculations, and we might, therefore, want to examine in detail the situations in which a NaN is produced. But there might be tens or hundreds or thousands of iterations, most of which do not generate NaN’s. Stepping through all the iterations would take a lifetime. What to do?

The solution is to trace the function using some of its more sophisticated options:

trace("nLL", quote(if(any(is.nan(lz))) { browser() }), at=4, print=F)

Here:

• nLL is the name of the function to be traced

• the stuff inside the quote function is code to be inserted (dynamically) into the nLL function; it could also be just the name of a function (as in our introductory examples)

• at is the line number at which to insert the code, which the author determines from as.list(body(nLL)) (see above)

• print=FALSE just suppresses the printing of a descriptive message

This is as if we had written:

    if(any(is.nan(lz))) {
        browser()
    }

at line 4 of the nLL function.

Here’s a simpler example using the functions we mentioned at the beginning of the discussion:

trace(h, quote(if (is.nan(r)) {
    browser()
}), at = 3)
f(7)
f(-3)
untrace(f)

Finally, if using the quoted expression for the second argument of trace seems too intimidating. let’s note that trace has an edit argument. If you call trace as:

    trace (someFn, ..., edit=TRUE)

you will dropped into an editor, where you can edit your code to your heart’s content before the (temporarily modified) function executes. BTW, you can also supply the name of an editor here, as:

    trace (someFn, ..., edit="/usr/bin/vim")

Editor’s Addendum: A quick way to enable bugging everywhere

One way to get in the habit of debugging is to drop this into your .Rprofile file or run it at the start of your R session:

options(error=utils::recover)

This will automatically invoke recover() every time your code gives an error, allowing you to view the stack of function calls and enter the browser mode in any of the functions. For instance:

> f("a")
Error in log(z) : non-numeric argument to mathematical function

Enter a frame number, or 0 to exit   

1: f("a")
2: #2: g(x)
3: #2: h(y)

Now selecting any of these will start browser mode within those functions. If you don’t need to use the browser (for instance, if the source of the error is obvious), just enter 0 and move on.